Match ferrybox sample numbers to stations

Find which sample numbers (in Ferrybox files) belongs to which stations

Corresponding numbers in Fantasy:
- VT4 = sample no 21-22

Corresponding numbers in Trollfjord:
- VT4 = sample no 23-24
- VT72 = sample no 21-22
- VT23 = sample no 17-18
- VT80 = sample no 11-12
- VT45 = sample no 13-14
- VT22 = sample no 15-16
- VR25 = sample no 6-7
- VR23 = sample no 4-5
- VR76 = sample no 2-3

For a version with interactive maps, see 21_FBstations.html.
(Was created using output: html_document with toc: true, toc_float: true to create html file.)

1. Libraries

library(plyr)      # used by functions in "Get_files_NIVA_ftp_server_functions.R" 
library(dplyr)     # load AFTER plyr, so e,g, we use count() from dplyr

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(purrr)

## 
## Attaching package: 'purrr'

## The following object is masked from 'package:plyr':
## 
##     compact

library(ggplot2)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:plyr':
## 
##     here

## The following object is masked from 'package:base':
## 
##     date

library(readxl)
library(tidyr)
library(RColorBrewer)

source("Get_files_NIVA_ftp_server_functions.R")

## Loading required package: bitops

## 
## Attaching package: 'RCurl'

## The following object is masked from 'package:tidyr':
## 
##     complete

# library(niRvana)
#source("12_QA_2019_from_excel_functions.R")
# RColorBrewer::display.brewer.all()

2. Read AqM station data

df_aqm_stations <- read_excel("Datasett/AqM_2017_2019_ØKOKYST_Ferrybox_ToR.xlsx", 
                              sheet = "StationPoint")

3. Ferrybox log data 2019

a. Ferrybox log data 2019

We do this before 2018 in order to get the headers (column names), which we will use for 2018 data
- Data downloaded from Pierre’s OneDrive 12.02.2020
- Link to Pierre’s OneDrive

df_ferrybox_sampledata_2019_fa <- readxl::read_excel(
  "Datasett/Ferrybox_samples_OneDrive/FA_2019_automatic_samples.xlsx")

df_ferrybox_sampledata_2019_tf <- readxl::read_excel(
  "Datasett/Ferrybox_samples_OneDrive/TF_2019_automatic_samples.xlsx")

# names(df_ferrybox_sampledata_2019_fa)
# names(df_ferrybox_sampledata_2019_tf)

X <- df_ferrybox_sampledata_2019_fa$SYSTEM_DATE_DMY - ymd_hms("1899-12-31 00:00:00")

df_ferrybox_sampledata_2019_fa <- df_ferrybox_sampledata_2019_fa %>%
  mutate(TIME = SYSTEM_TIME + X)

# Check
# df_samples_fa$SYSTEM_DATE_DMY %>% head()
# df_samples_fa$SYSTEM_TIME %>% head()
# df_samples_fa$TIME %>% head()

unique(df_ferrybox_sampledata_2019_fa$SYSTEM_DATE_DMY)

##  [1] "2018-12-31 UTC" "2019-01-03 UTC" "2019-01-04 UTC" "2019-02-18 UTC"
##  [5] "2019-02-19 UTC" "2019-02-26 UTC" "2019-02-27 UTC" "2019-03-06 UTC"
##  [9] "2019-03-07 UTC" "2019-03-20 UTC" "2019-03-21 UTC" "2019-04-09 UTC"
## [13] "2019-04-10 UTC" "2019-04-12 UTC" "2019-04-23 UTC" "2019-04-24 UTC"
## [17] "2019-05-13 UTC" "2019-05-14 UTC" "2019-05-29 UTC" "2019-05-30 UTC"
## [21] "2019-06-08 UTC" "2019-06-09 UTC" "2019-06-26 UTC" "2019-06-27 UTC"
## [25] "2019-07-01 UTC" "2019-07-14 UTC" "2019-07-15 UTC" "2019-07-30 UTC"
## [29] "2019-07-31 UTC" "2019-08-15 UTC" "2019-08-16 UTC" "2019-09-02 UTC"
## [33] "2019-09-03 UTC" "2019-09-08 UTC" "2019-09-09 UTC" "2019-09-22 UTC"
## [37] "2019-09-23 UTC" "2019-10-02 UTC" "2019-10-03 UTC" "2019-10-16 UTC"
## [41] "2019-10-17 UTC" "2019-10-28 UTC" "2019-10-29 UTC" "2019-11-17 UTC"
## [45] "2019-11-18 UTC" "2019-11-27 UTC" "2019-11-28 UTC" "2019-12-09 UTC"
## [49] "2019-12-10 UTC" "2019-12-17 UTC" "2019-12-18 UTC"

b. Ferrybox log data 2018

# 4. Download all Ferrybox log data    
# Or just use saved log data

redownload_data <- FALSE  # if FALSE, we just read the saved data


if (redownload_data){  # takes just a couple of minutes
  
  #
  # a. Get dates and filenames ('df_samplefilenames_fa_2018')  
  #
  # get_folders()
  
  #
  # Fantasy
  #
  
  # Get the url for the vessel we want
  url.folder <- get_url_foldername("fantasy")
  
  # Get file names of all log files for that vessel
  samplefiles_fn <- get_filenames_samplefiles("fantasy")
  
  head(samplefiles_fn)
  # Check
  # tail(logfiles_fn)
  
  # Combine with dates 
  df_samplefilenames_fa_2018 <- data.frame(
    filename = samplefiles_fn,
    Date = ymd(substr(samplefiles_fn, 9, 16)),   # note that 9,16 is specifically for 'samples'
    stringsAsFactors = FALSE) %>%
    filter(year(Date) == 2018)
  
  
  #
  # Trollfjord
  #
  
  # Get the url for the vessel we want
  url.folder <- get_url_foldername("trollfjord")
  
  # Get file names of all log files for that vessel
  samplefiles_fn <- get_filenames_samplefiles("trollfjord")
  
  # Check
  # tail(logfiles_fn)
  
  # Combine with dates 
  df_samplefilenames_tf_2018 <- data.frame(
    filename = samplefiles_fn,
    Date = ymd(substr(samplefiles_fn, 9, 16)), # note that 9,16 is specifically for 'samples'
    stringsAsFactors = FALSE) %>%
    filter(year(Date) == 2018)
  
  #
  # b. Combine filenames  
  #
  
  df_samplefilenames_2018 <- 
    bind_rows(
      df_samplefilenames_fa_2018 %>% mutate(Ferrybox = "fantasy"),
      df_samplefilenames_tf_2018 %>% mutate(Ferrybox = "trollfjord")
    )
  
  #
  # c. Download sample file data
  #
  df_ferrybox_sampledata_2018_fa <- get_several_logfiles(
    df_samplefilenames_fa_2018$filename, 
    vessel = "fantasy",
    trace = FALSE)
  
  df_ferrybox_sampledata_2018_tf <- get_several_logfiles(
    df_samplefilenames_tf_2018$filename, 
    vessel = "trollfjord",
    trace = FALSE)
  
  names(df_ferrybox_sampledata_2018_fa) <- names(df_ferrybox_sampledata_2019_fa)
  names(df_ferrybox_sampledata_2018_tf) <- names(df_ferrybox_sampledata_2019_tf)

  #
  # d. Save
  #
  saveRDS(df_ferrybox_sampledata_2018_fa,
          "Datasett/18_df_ferrybox_sampledata_2018_fa.rds")
  saveRDS(df_ferrybox_sampledata_2018_tf,
          "Datasett/18_df_ferrybox_sampledata_2018_tf.rds")
  
  
} else {
  
  df_ferrybox_sampledata_2018_fa <-
    readRDS("Datasett/18_df_ferrybox_sampledata_2018_fa.rds")
  df_ferrybox_sampledata_2018_tf <-
    readRDS("Datasett/18_df_ferrybox_sampledata_2018_tf.rds")
  
}

c. Combine data for both years for each ferry

2018 + 2019
The file columns differ betrween ferries, so we donæt combine them

#
# Fantasy
#

df_ferrybox_sampledata_2018_fa[1:7] %>% str()

## 'data.frame':    571 obs. of  7 variables:
##  $ SHIP_CODE      : chr  "FA" "FA" "FA" "FA" ...
##  $ SYSTEM_DATE_DMY: chr  "27.02.2018" "27.02.2018" "27.02.2018" "27.02.2018" ...
##  $ SYSTEM_TIME    : chr  "14:00:40" "14:09:41" "17:18:00" "17:22:01" ...
##  $ GPS_LATITUDE   : num  54.5 54.5 55.4 55.4 56.1 ...
##  $ GPS_LONGITUDE  : num  10.4 10.4 11 11 11.2 ...
##  $ GPS_TIME       : int  140037 140938 171757 172158 195914 200414 221327 221728 235438 235838 ...
##  $ PUMP           : int  1 1 1 1 1 1 1 1 1 1 ...

df_ferrybox_sampledata_2019_fa[1:7] %>% str()

## Classes 'tbl_df', 'tbl' and 'data.frame':    600 obs. of  7 variables:
##  $ SHIP_CODE      : chr  "FA" "FA" "FA" "FA" ...
##  $ SYSTEM_DATE_DMY: POSIXct, format: "2018-12-31" "2019-01-03" ...
##  $ SYSTEM_TIME    : POSIXct, format: "1899-12-31 08:30:14" "1899-12-31 14:05:20" ...
##  $ GPS_LATITUDE   : num  54.3 54.5 54.5 55.4 55.4 ...
##  $ GPS_LONGITUDE  : num  10.2 10.4 10.4 11 11 ...
##  $ GPS_TIME       : num  83013 140519 141520 172040 172440 ...
##  $ PUMP           : num  0 0 0 0 0 1 0 0 1 1 ...

df_ferrybox_sampledata_2018_fa <- df_ferrybox_sampledata_2018_fa %>%
  mutate(SYSTEM_DATE_DMY = dmy_hms(paste(SYSTEM_DATE_DMY, "00:00:00")),
         SYSTEM_TIME = ymd_hms(paste("1899-12-31", SYSTEM_TIME)))

df_ferrybox_sampledata_fa <- bind_rows(
  df_ferrybox_sampledata_2018_fa,
  df_ferrybox_sampledata_2019_fa)

#
# Trollfjord
#
df_ferrybox_sampledata_2018_tf[1:7] %>% str()

## 'data.frame':    680 obs. of  7 variables:
##  $ SHIP_CODE  : chr  "TF" "TF" "TF" "TF" ...
##  $ SYSTEM_DATE: chr  "26.03.2018" "26.03.2018" "26.03.2018" "26.03.2018" ...
##  $ SYSTEM_TIME: chr  "08:49:43" "08:51:43" "08:57:44" "08:59:44" ...
##  $ GPS_LAT    : num  70.7 70.7 70.7 70.7 70.7 ...
##  $ GPS_LON    : num  23.7 23.7 23.7 23.7 23.7 ...
##  $ GPS_TIME   : int  84938 85138 85739 85939 90240 90340 90540 90741 90941 91141 ...
##  $ PUMP_FLAG  : int  0 0 0 0 0 0 0 0 0 0 ...

df_ferrybox_sampledata_2019_tf[1:7] %>% str()

## Classes 'tbl_df', 'tbl' and 'data.frame':    260 obs. of  7 variables:
##  $ SHIP_CODE  : chr  "TF" "TF" "TF" "TF" ...
##  $ SYSTEM_DATE: POSIXct, format: "2018-12-20" "2019-01-16" ...
##  $ SYSTEM_TIME: POSIXct, format: "1899-12-31 19:53:26" "1899-12-31 11:58:09" ...
##  $ GPS_LAT    : num  63 69.8 69.8 69.8 70.5 ...
##  $ GPS_LON    : num  7.26 30.1 30.11 30.11 30.99 ...
##  $ GPS_TIME   : num  195322 115806 120707 121108 161343 ...
##  $ PUMP_FLAG  : num  1 1 1 1 1 1 1 1 1 1 ...

df_ferrybox_sampledata_2018_tf <- df_ferrybox_sampledata_2018_tf %>%
  mutate(SYSTEM_DATE = dmy_hms(paste(SYSTEM_DATE, "00:00:00")),
         SYSTEM_TIME = ymd_hms(paste("1899-12-31", SYSTEM_TIME)))

df_ferrybox_sampledata_tf <- bind_rows(
  df_ferrybox_sampledata_2018_tf,
  df_ferrybox_sampledata_2019_tf)

names(df_ferrybox_sampledata_fa)

##  [1] "SHIP_CODE"                  "SYSTEM_DATE_DMY"           
##  [3] "SYSTEM_TIME"                "GPS_LATITUDE"              
##  [5] "GPS_LONGITUDE"              "GPS_TIME"                  
##  [7] "PUMP"                       "OBSTRUCTION"               
##  [9] "MANUAL_SAMPLE_COUNTER"      "AUTOMATIC_SAMPLE_COUNTER"  
## [11] "DATA_FLAG_UNDERWAY"         "DATA_QUALITY_UNDERWAY"     
## [13] "TRIP_NUMBER"                "TURBIDITY"                 
## [15] "NOT_AVAILABLE"              "RAW_CHLA_FLUORESCENCE"     
## [17] "CHLA_FLUORESCENCE"          "INLET_TEMPERATURE"         
## [19] "CTD_TEMPERATURE"            "CTD_SALINITY"              
## [21] "OXYGEN_CONCENTRATION"       "OXYGEN_SATURATION"         
## [23] "OXYGEN_TEMPERATURE"         "NOT_AVAILABLE__1"          
## [25] "RAW_CDOM_FLUORESCENCE"      "CDOM_FLUORESCENCE"         
## [27] "RAW_CYANO_FLUORESCENCE"     "CYANO_FLUORESCENCE"        
## [29] "INLET_OXYGEN_SATURATION"    "INLET_OXYGEN_CONCENTRATION"
## [31] "INLET_OXYGEN_TEMPERATURE"   "RAW_PAH_FLUORESCENCE"      
## [33] "PAH_FLUORESCENCE"           "TIME"

names(df_ferrybox_sampledata_tf)

##  [1] "SHIP_CODE"       "SYSTEM_DATE"     "SYSTEM_TIME"     "GPS_LAT"        
##  [5] "GPS_LON"         "GPS_TIME"        "PUMP_FLAG"       "OBS_FLAG"       
##  [9] "SAMPLE_MAN_NUM"  "SAMPLE_AUTO_NUM" "DATA_VALID_FLAG" "DATA_QA_FLAG"   
## [13] "TRIP"            "TURB"            "NOT_AVAILABLE"   "RAW_CHLA_FLU"   
## [17] "CHLA_FLU"        "TEMP_INLET"      "TEMP_CTD"        "SAL_CTD"        
## [21] "CONC_O2"         "SAT_O2"          "TEMP_O2"         "PRESS"          
## [25] "RAW_YEL_FLU"     "YEL_FLU"         "RAW_BG_FLU"      "BG_FLU"

# df_ferrybox_sampledata <- 
#   bind_rows()

d. Save sample files

saveRDS(df_ferrybox_sampledata_fa,
        "Datasett/18_df_ferrybox_sampledata_fa.rds")
saveRDS(df_ferrybox_sampledata_tf,
        "Datasett/18_df_ferrybox_sampledata_tf.rds")

openxlsx::write.xlsx(df_ferrybox_sampledata_fa,
                     "Datasett/Ferrybox_samples_OneDrive/FA_2018-19_automatic_samples.xlsx")

## Note: zip::zip() is deprecated, please use zip::zipr() instead

openxlsx::write.xlsx(df_ferrybox_sampledata_tf,
                     "Datasett/Ferrybox_samples_OneDrive/TF_2018-19_automatic_samples.xlsx")

e. Sample numbers used in files

Fantasy:
- Mostly MANUAL_SAMPLE_COUNTER = 0 and AUTOMATIC_SAMPLE_COUNTER = 1-24
- For a few lines MANUAL_SAMPLE_COUNTER = 1-24 and AUTOMATIC_SAMPLE_COUNTER = 0
Trollfjord:
- Same but variables are called SAMPLE_MAN_NUM and SAMPLE_AUTO_NUM

cat("================================================\n")
cat("     FANTASY\n")
#
# Fantasy
#
cat("----------------------------\n")
cat("MANUAL_SAMPLE_COUNTER == 0\n")
df_ferrybox_sampledata_fa %>% 
  filter(MANUAL_SAMPLE_COUNTER == 0) %>% 
  xtabs(~AUTOMATIC_SAMPLE_COUNTER, .)

cat("----------------------------\n")
cat("AUTOMATIC_SAMPLE_COUNTER == 0\n")
df_ferrybox_sampledata_fa %>% 
  filter(AUTOMATIC_SAMPLE_COUNTER == 0) %>% 
  xtabs(~MANUAL_SAMPLE_COUNTER, .)

cat("----------------------------\n")
cat("Both == 0\n")
df_ferrybox_sampledata_fa %>% 
  filter(MANUAL_SAMPLE_COUNTER == 0 & AUTOMATIC_SAMPLE_COUNTER == 0) %>% nrow()

cat("================================================\n")
cat("     TROLLFJORD\n")
#
# Trollfjord
#

cat("----------------------------\n")
cat("SAMPLE_MAN_NUM == 0\n")
df_ferrybox_sampledata_tf %>% 
  filter(SAMPLE_MAN_NUM == 0) %>%
  xtabs(~SAMPLE_AUTO_NUM, .)

cat("----------------------------\n")
cat("SAMPLE_AUTO_NUM == 0\n")
df_ferrybox_sampledata_tf %>% 
  filter(SAMPLE_AUTO_NUM == 0) %>%
  xtabs(~SAMPLE_MAN_NUM, .)

## ================================================
##      FANTASY
## ----------------------------
## MANUAL_SAMPLE_COUNTER == 0
## AUTOMATIC_SAMPLE_COUNTER
##  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 
## 53 44 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 
## ----------------------------
## AUTOMATIC_SAMPLE_COUNTER == 0
## MANUAL_SAMPLE_COUNTER
##  0  1  2  3  4  5  6  7 10 11 13 14 15 16 17 19 20 22 23 24 
## 53  6  6  4  3  4  3  1  1  1  1  1  1  1  1  1  1  1  1  1 
## ----------------------------
## Both == 0
## [1] 53
## ================================================
##      TROLLFJORD
## ----------------------------
## SAMPLE_MAN_NUM == 0
## SAMPLE_AUTO_NUM
##   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19 
## 184  24  25  25  23  25  24  27  50  23  26  26  26  26  26  25  47  25  28  27 
##  20  21  22  23  24 
##  26  28  37  28  56 
## ----------------------------
## SAMPLE_AUTO_NUM == 0
## SAMPLE_MAN_NUM
##   0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19 
## 184   1   5   4   4   2   1   2   2   1   2   3   1   3   2   1   1   1   1   1 
##  20  21  22  23  24  25  26  28  31 
##   1   1   2   1   1   1   2   1   1

4. Match automatic samples

Find which sample numbers (in Ferrybox files) belongs to which stations

Check samples in map - prepare data

library(mapview)
library(sf)

## Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3

#
# Fantasy (sf_points_fa)
#

# Set rownames to "FullStationName" - these will show up when you hover over points with the mouse
df_points <- df_ferrybox_sampledata_fa %>%
  filter(AUTOMATIC_SAMPLE_COUNTER > 0) %>%
  # "copy" coordinates so the show up in popup
  mutate(Lon = GPS_LONGITUDE, Lat = GPS_LATITUDE) %>%  
  as.data.frame()   # read_excel makes a tibble, but you can't set row names for tibbles 

# Set rownames (shown at mouse hover) - must be unique
rownames(df_points) <- with(df_points,
                            paste(SHIP_CODE, AUTOMATIC_SAMPLE_COUNTER, 
                                  SYSTEM_DATE_DMY))

sf_points_fa <- st_as_sf(df_points,
                          coords = c("GPS_LONGITUDE", "GPS_LATITUDE"),
                          crs = "+proj=longlat +ellps=WGS84")

#
# Trollfjord (sf_points_tf)
#

# Set rownames to "FullStationName" - these will show up when you hover over points with the mouse
df_points <- df_ferrybox_sampledata_tf %>%
  filter(SAMPLE_AUTO_NUM > 0) %>%
  # "copy" coordinates so the show up in popup
  mutate(Lon = GPS_LON, Lat = GPS_LAT) %>%  
  as.data.frame()   # read_excel makes a tibble, but you can't set row names for tibbles 

# Set rownames (shown at mouse hover) - must be unique
rownames(df_points) <- with(df_points,
                            paste(SHIP_CODE, SAMPLE_AUTO_NUM, 
                                  SYSTEM_DATE, SYSTEM_TIME))

sf_points_tf <- st_as_sf(df_points,
                          coords = c("GPS_LON", "GPS_LAT"),
                          crs = "+proj=longlat +ellps=WGS84")

Check Fantasy samples in map

m_fa <- mapview(sf_points_fa, alpha.regions = 0.2)
m_fa

Check Trollfjord samples in map

m_tf <- mapview(sf_points_tf, alpha.regions = 0.2)
m_tf

Check stations in map

df_aqm_stations <- as.data.frame(df_aqm_stations)

rownames(df_aqm_stations) <- df_aqm_stations$StationCode  

sf_points_stations <- st_as_sf(df_aqm_stations,
                       coords = c("Longitude", "Latitude"),
                       crs = "+proj=longlat +ellps=WGS84")

mapview(sf_points_stations)

Stations vs Fantasy sample positions

Correspondence:
- VT4 = sample no 21-22

m_fa +mapview(sf_points_stations, color = "red")

Stations vs Trollfjord sample positions

Correspondence:
- VT4 = sample no 23-24
- VT72 = sample no 21-22
- VT23 = sample no 17-18
- VT80 = sample no 11-12
- VT45 = sample no 13-14
- VT22 = sample no 15-16
- VR25 = sample no 6-7
- VR23 = sample no 4-5
- VR76 = sample no 2-3

m_tf + mapview(sf_points_stations, color = "red")

5. Check positions of manual samples

Check samples in map - prepare data

library(mapview)
library(sf)

#
# Fantasy (sf_points_fa)
#

# Set rownames to "FullStationName" - these will show up when you hover over points with the mouse
df_points <- df_ferrybox_sampledata_fa %>%
  filter(MANUAL_SAMPLE_COUNTER > 0) %>%
  # "copy" coordinates so the show up in popup
  mutate(Lon = GPS_LONGITUDE, Lat = GPS_LATITUDE) %>%  
  as.data.frame()   # read_excel makes a tibble, but you can't set row names for tibbles 

# Set rownames (shown at mouse hover) - must be unique
rownames(df_points) <- with(df_points,
                            paste(SHIP_CODE, MANUAL_SAMPLE_COUNTER, 
                                  SYSTEM_DATE_DMY, SYSTEM_TIME))

sf_points_fa <- st_as_sf(df_points,
                          coords = c("GPS_LONGITUDE", "GPS_LATITUDE"),
                          crs = "+proj=longlat +ellps=WGS84")

#
# Trollfjord (sf_points_tf)
#

# Set rownames to "FullStationName" - these will show up when you hover over points with the mouse
df_points <- df_ferrybox_sampledata_tf %>%
  filter(SAMPLE_MAN_NUM > 0) %>%
  # "copy" coordinates so the show up in popup
  mutate(Lon = GPS_LON, Lat = GPS_LAT) %>%  
  as.data.frame()   # read_excel makes a tibble, but you can't set row names for tibbles 

# Set rownames (shown at mouse hover) - must be unique
rownames(df_points) <- with(df_points,
                            paste(SHIP_CODE, SAMPLE_MAN_NUM, 
                                  SYSTEM_DATE, SYSTEM_TIME))

sf_points_tf <- st_as_sf(df_points,
                          coords = c("GPS_LON", "GPS_LAT"),
                          crs = "+proj=longlat +ellps=WGS84")

Check Fantasy samples in map

m_fa <- mapview(sf_points_fa, alpha.regions = 0.2)
m_fa

Check Trollfjord samples in map

m_fa <- mapview(sf_points_tf, alpha.regions = 0.2)
m_fa

Match ferrybox sample numbers to stations

Helene

7 februar 2020

1. Libraries

2. Read AqM station data

3. Ferrybox log data 2019

a. Ferrybox log data 2019

b. Ferrybox log data 2018

c. Combine data for both years for each ferry

d. Save sample files

e. Sample numbers used in files

4. Match automatic samples

Check samples in map - prepare data

Check Fantasy samples in map

Check Trollfjord samples in map

Check stations in map

Stations vs Fantasy sample positions

Stations vs Trollfjord sample positions

5. Check positions of manual samples

Check samples in map - prepare data

Check Fantasy samples in map

Check Trollfjord samples in map